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零样本学习研究进展

张鲁宁 左信 刘建伟

张鲁宁, 左信, 刘建伟. 零样本学习研究进展[J]. 仁和测试, 2020, 46(1): 1-23. doi: 10.16383/j.aas.c180429
引用本文: 张鲁宁, 左信, 刘建伟. 零样本学习研究进展[J]. 仁和测试, 2020, 46(1): 1-23. doi: 10.16383/j.aas.c180429
Lu-Ning ZHANG, Xin ZUO, Jian-Wei LIU. Research and Development on Zero-Shot Learning[J]. Rhhz Test, 2020, 46(1): 1-23. doi: 10.16383/j.aas.c180429
Citation: Lu-Ning ZHANG, Xin ZUO, Jian-Wei LIU. Research and Development on Zero-Shot Learning[J]. Rhhz Test, 2020, 46(1): 1-23. doi: 10.16383/j.aas.c180429

零样本学习研究进展

doi: 10.16383/j.aas.c180429
基金项目: 

国家重点研发计划项目 2016YFC0303703-03

中国石油大学(北京)年度前瞻导向及培育项目 2462018QZDX02

详细信息
    作者简介:

    张鲁宁   中国石油大学(北京)自动化系博士研究生.2016年获得中国石油大学(北京)自动化系学士学位.主要研究方向为零样本学习与点过程学习. E-mail:zhang.luning@163.com

    刘建伟  中国石油大学(北京)自动化系副研究员.主要研究方向为模式识别与智能系统, 先进控制. E-mail:liujw@cup.edu.cn

    通讯作者:

    左信  中国石油大学(北京)自动化系教授.主要研究方向为智能控制, 安全仪表系统的分析和设计, 先进过程控制.本文通信作者.E-mail:zuox@cup.edu.cn

  • 本文责任编委  张敏灵

Research and Development on Zero-Shot Learning

Funds: 

National Key Research and Development Program 2016YFC0303703-03

China University of Petroleum (Beijing) Prospective Orientation and Cultivation Project 2462018QZDX02

More Information
    Author Bio:

    ZHANG Lu-Ning   Ph. D. candidate in the Department of Automation, China University of Petroleum (Beijing).He received his bachelor degree from the Department of Automation, China University of Petroleum (Beijing) in 2016. His research interest covers zero-shot learning and point-process learning

    LIU Jian-Wei   Associate researcher in the Department of Automation, China University of Petroleum (Beijing).His research interest covers pattern recognition and intelligent system, and advanced control

    Corresponding author: ZUO Xin   Professor in the Department of Automation, China University of Petroleum (Beijing). His research interest covers intelligent control, analysis and design of safety instumented system, and advanced process control. Corresponding author of this paper
  • Recommended by Associate Editor ZHANG Min-Ling
  • 摘要: 近几年来, 深度学习在机器学习研究领域中取得了巨大的突破, 深度学习能够很好地实现复杂问题的学习, 然而, 深度学习最大的弊端之一, 就是需要大量人工标注的训练数据, 而这需要耗费大量的人力成本.因此, 为了缓解深度学习存在的这一问题, Palatucci等于2009年提出了零样本学习(Zero-shot learning).零样本学习是迁移学习的一种特殊场景, 在零样本学习过程中, 训练类集和测试类集之间没有交集, 需要通过训练类与测试类之间的知识迁移来完成学习, 使在训练类上训练得到的模型能够成功识别测试类输入样例的类标签.零样本学习的意义不仅在于可以对难以标注的样例进行识别, 更在于这一方法模拟了人类对于从未见过的对象的认知过程, 零样本学习方法的研究, 也会在一定程度上促进认知科学的研究.鉴于零样本学习的应用价值、理论意义和未来的发展潜力, 文中系统综述了零样本学习的研究进展, 首先概述了零样本学习的定义, 介绍了4种典型的零样本学习模型, 并对零样本学习存在的关键问题及解决方法进行了介绍, 对零样本学习的多种模型进行了分类和阐述, 并在最后指明了零样本学习进一步研究中需要解决的问题以及未来可能的发展方向.
    Recommended by Associate Editor ZHANG Min-Ling
    1)  本文责任编委  张敏灵
  • 图  1  零样本学习结构示意图

    Figure  1.  Zero-shot learning structure

    图  2  输入空间方法示意图

    Figure  2.  Input space method

    图  3  模型空间方法示意图

    Figure  3.  Model space method

    图  4  语义输出编码零样本学习过程示意图

    Figure  4.  Semantic output code zero-shot learning process

    图  5  直接属性预测模型结构示意图

    Figure  5.  Direct attribute prediction model

    图  6  间接属性预测模型结构示意图

    Figure  6.  Indirect attribute prediction model

    图  7  跨模态迁移零样本学习示意图

    Figure  7.  Cross-modal zero-shot learning

    图  8  枢纽化问题示意图

    Figure  8.  Hubness

    图  9  映射域偏移问题示意图

    Figure  9.  The projection domain shift problem

    图  10  相容性模型示意图

    Figure  10.  Compatibility model

    图  11  混合模型示意图

    Figure  11.  Hybrid model

    图  12  线性相容性模型分类示意图

    Figure  12.  Linear compatibility model classification

    图  13  非线性相容性模型分类示意图

    Figure  13.  Nonlinear compatibility model classification

    图  14  混合模型分类示意图

    Figure  14.  Hybrid model classification

    图  15  语义自编码器零样本学习示意图

    Figure  15.  Semantic autoencoder zero-shot learning

    图  16  语义相似嵌入模型零样本学习示意图

    Figure  16.  Semantic similarity embedding zero-shot learning model

    表  1  5种数据集属性介绍

    Table  1.   Introduction to the attributes of the five datasets

    数据集 AWA CUB aPY SUN AwA2
    图像个数 30 475 11788 15 539 14 340 37 322
    类个数 50 200 32 17 50
    属性个数 85 312 64 102 85
    注释水平 每一类 每张图片 每张图片 每张图片 每一类
    注释类型(实值或布尔值) 兼有 兼有 兼有 布尔 兼有
    下载: 导出CSV

    表  2  多个模型在4个数据集下的实验结果

    Table  2.   Experimental results of the models under four data

    模型一数据集(%) AWA CUB aPY SUN
    DAP[14] 41.4 28.3 \ 19.1
    IAP[14] 42.2 24.4 \ 16.9
    ESZSL[23] 49.3 \ 65.8*\18.7 15.1
    SYNC[46] 69.7 53.4 62.8 \
    SSE[64] 76.3 30.4 82.5* 46. 2
    LATEM[62] 71.9 45. 5 \ \
    SJE[57] 66.7 50.1 56.1 \
    SAE[59] 84.7 61.4 91.0*\65.2 54.8
    下载: 导出CSV
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  • 收稿日期:  2018-06-15
  • 录用日期:  2018-08-30
  • 刊出日期:  2020-03-07

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